Facial Expression Recognition for HCI Applications

Facial Expression Recognition for HCI Applications

Fadi Dornaika (Institut Géographique National, France) and Bogdan Raducanu (Computer Vision Center, Spain)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch095
OnDemand PDF Download:
$37.50

Abstract

Facial expression plays an important role in cognition of human emotions (Fasel, 2003 & Yeasin, 2006). The recognition of facial expressions in image sequences with significant head movement is a challenging problem. It is required by many applications such as human-computer interaction and computer graphics animation (Cañamero, 2005 & Picard, 2001). To classify expressions in still images many techniques have been proposed such as Neural Nets (Tian, 2001), Gabor wavelets (Bartlett, 2004), and active appearance models (Sung, 2006). Recently, more attention has been given to modeling facial deformation in dynamic scenarios. Still image classifiers use feature vectors related to a single frame to perform classification. Temporal classifiers try to capture the temporal pattern in the sequence of feature vectors related to each frame such as the Hidden Markov Model based methods (Cohen, 2003, Black, 1997 & Rabiner, 1989) and Dynamic Bayesian Networks (Zhang, 2005). The main contributions of the paper are as follows. First, we propose an efficient recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of a robot whose response is generated according to the user’s recognized facial expression. Our proposed approach has several advantages. First, unlike most expression recognition systems that require a frontal view of the face, our system is viewand texture-independent. Second, its learning phase is simple compared to other techniques (e.g., the Hidden Markov Models and Active Appearance Models), that is, we only need to fit second-order Auto-Regressive models to sequences of facial actions. As a result, even when the imaging conditions change the learned Auto-Regressive models need not to be recomputed. The rest of the paper is organized as follows. Section 2 summarizes our developed appearance-based 3D face tracker that we use to track the 3D head pose as well as the facial actions. Section 3 describes the proposed facial expression recognition based on the detection of keyframes. Section 4 provides some experimental results. Section 5 describes the proposed human-machine interaction application that is based on the developed facial expression recognition scheme.
Chapter Preview
Top

Introduction

Facial expression plays an important role in cognition of human emotions (Fasel, 2003 & Yeasin, 2006). The recognition of facial expressions in image sequences with significant head movement is a challenging problem. It is required by many applications such as human-computer interaction and computer graphics animation (Cañamero, 2005 & Picard, 2001). To classify expressions in still images many techniques have been proposed such as Neural Nets (Tian, 2001), Gabor wavelets (Bartlett, 2004), and active appearance models (Sung, 2006). Recently, more attention has been given to modeling facial deformation in dynamic scenarios. Still image classifiers use feature vectors related to a single frame to perform classification. Temporal classifiers try to capture the temporal pattern in the sequence of feature vectors related to each frame such as the Hidden Markov Model based methods (Cohen, 2003, Black, 1997 & Rabiner, 1989) and Dynamic Bayesian Networks (Zhang, 2005). The main contributions of the paper are as follows. First, we propose an efficient recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of a robot whose response is generated according to the user’s recognized facial expression.

Our proposed approach has several advantages. First, unlike most expression recognition systems that require a frontal view of the face, our system is view- and texture-independent. Second, its learning phase is simple compared to other techniques (e.g., the Hidden Markov Models and Active Appearance Models), that is, we only need to fit second-order Auto-Regressive models to sequences of facial actions. As a result, even when the imaging conditions change the learned Auto-Regressive models need not to be recomputed. The rest of the paper is organized as follows. Section 2 summarizes our developed appearance-based 3D face tracker that we use to track the 3D head pose as well as the facial actions. Section 3 describes the proposed facial expression recognition based on the detection of keyframes. Section 4 provides some experimental results. Section 5 describes the proposed human-machine interaction application that is based on the developed facial expression recognition scheme.

Key Terms in this Chapter

Human–Computer Interaction (HCI): The study of interaction between people (users) and computers. It is an interdisciplinary subject, relating computer science with many other fields of study and research (Artificial Intelligence, Psychology, Computer Graphics, Design)

Hidden Markov Model (HMM): Statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications

3D Deformable Model: A model which is able to modify its shape while being acted upon by an external influence. In consequence, the relative position of any point on a deformable body can change

Active Appearance Models (AAM): Computer Vision algorithm for matching a statistical model of object shape and appearance to a new image. The approach is widely used for matching and tracking faces.

Social Robot: An autonomous robot that interacts and communicates with humans by following the social rules attached to its role. This definition implies that a social robot has a physical embodiment. A consequence of the previous statements is that a robot that only interacts and communicates with other robots would not be considered to be a social robot.

Facial Expression Recognition System: Computer-driven application for automatically identifying person’s facial expression from a digital still or video image. It does that by comparing selected facial features in the live image and a facial database.

Autoregressive Models: Group of linear prediction formulas that attempt to predict the output of a system based on the previous outputs and inputs.

Wireframe Model: The representation of all surfaces of a three-dimensional object in outline form.

AIBO: One of several types of robotic pets designed and manufactured by Sony. Able to walk, “see” its environment via camera, and recognize spoken commands, they are considered to be autonomous robots, since they are able to learn and mature based on external stimuli from their owner or environment, or from other AIBOs

Complete Chapter List

Search this Book:
Reset